Source: techradar.com
The time for experiments is over: Artificial Intelligence (AI) solutions are ready for the enterprise. Already, the early adopters are reaping the rewards and moving from trials to large-scale adoption. The chief executives of these companies are confident that – within just two years – AI deployments will deliver around 30% of both their new revenues and cost savings.
Other businesses are now following suit, eager not to miss out on the AI advantage. I’m certain that companies which delay drafting and delivering their own AI strategies are bound for obsolescence. However, to succeed in an AI world, companies first have to get three things right: governance, talent and skills.
Introducing AI in an enterprise is more than an IT upgrade; it’s a revolution and arguably one of the most important IT decisions that a company will make. That’s why it requires more strategic planning and consideration than any previous shift in computing – whether that was the introduction of mobile devices or the move from private to hybrid and public cloud computing.
For company boards, this means that setting and approving an AI strategy is the most important business decision they now have to make. When it comes to AI, moving fast and breaking things simply isn’t a good enough technology strategy.
The failure of strategic insight
In too many companies, however, board members simply have yet to acquire the AI knowledge and strategic insight they need to make the right decisions. Just to be clear, you don’t have to be a data scientist to get it right; what board members need is a good understanding of both the opportunities and risks of introducing AI in the businesses – because if they get it wrong, they could face both a huge failure of corporate governance and a total meltdown of corporate trust.
Board members must bootstrap their AI knowledge now. If they fail to put the right policies in place from the outset, they are setting up their companies for a world of trouble and even could harm AI’s wider acceptance by society. At Infosys, we are now offering tailored “AI bootcamps” to the boards of our customers, and I want to extend this offer to companies beyond, because we know that while AI has the power to transform our world, it has to be done right.
AI can only deliver maximum impact and avoid IT brownfield challenges if companies completely update and fully integrate their technology and operating models. Bolt-on solutions don’t make an AI strategy. My second piece of advice to boards is that they should urgently develop and then monitor strong ethics guidelines for the use of AI and Big Data in their enterprise.
That’s especially important now, as more generic AI solutions are making way for highly-specialized AI tools. I believe that company boards have to dedicate at least as much focus on the ethics and governance of AI and machine learning as they bring to identifying the right technical solutions.
Tackling the talent bottleneck
Beyond governance of Big Data and AI, there’s a second bottleneck and that’s talent. The well-worn phrase is true: every business is a technology company now; soon, though, most will also be AI companies.
So when it comes to hiring good data scientists and AI experts, these businesses will have to compete not only with their peers but also tech giants like Facebook, Amazon and Google. Instead of attempting to raid the physics and mathematics departments of their local universities for talent, I therefore recommend that companies look elsewhere for AI experts – on their own payroll.
Most businesses have incredible talent in-house. All they have to do is provide their staff with the necessary training and support, which can be done with the help of technology partners, provided these are platform-agnostic so that they can support a wide range of technologies and use cases.
Training will have to be delivered on two levels. The first is AI enablement, by training staff to program and handle the technical aspects of AI and machine learning; they need to understand how to use bots, deploy robotic process automation and use machine learning to harness big data. Already, setting up virtual agents and AI bots does not require deep AI expertise anymore but can be done in low-code, self-service IT environments.Advertisement
Companies with more demanding AI needs will need to go to the second level, building AI competence by offering deep-dive courses into data science, machine learning and the intricate workings of various AI solutions. To have impact, these courses need to be production focused and rooted in real-life examples.
Infusing AI agility
Training your own experts is vital, of course, but in my experience does not go far enough. Every employee needs to have fundamental AI skills. By introducing everybody to the AI basics will both tackle potential misconceptions about the impact of AI on people’s lives and jobs and help drive the business case for AI. The use case for AI is not just in process automation, but the augmentation of the workforce with the power of AI – and that affects everybody.
Only by addressing all three challenges – governance, talent and skills – will we be able to build enterprises that are truly AI native and deliver on the huge opportunities offered by AI and machine learning.
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